270 lines
7.9 KiB
Plaintext
270 lines
7.9 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "3bc892c3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"hello world\n"
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]
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}
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],
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"source": [
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"print(\"hello world\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "725bdfc5",
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"metadata": {},
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"source": [
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"This is a markdown tile. Testing. this."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "89eea5d5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>OrgID</th>\n",
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" <th>Organization</th>\n",
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" <th>Churn Date</th>\n",
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" <th>2022-01</th>\n",
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" <th>2022-02</th>\n",
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" <th>2022-03</th>\n",
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" <th>2022-04</th>\n",
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" <th>2022-05</th>\n",
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" <th>2022-06</th>\n",
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" <th>2022-07</th>\n",
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" <th>2022-08</th>\n",
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" <th>2022-09</th>\n",
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" <th>2022-10</th>\n",
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" <th>2022-11</th>\n",
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" <th>2022-12</th>\n",
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" <th>2023-01</th>\n",
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" <th>2023-02</th>\n",
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" <th>2023-03</th>\n",
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" <th>2023-04</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>30867753</td>\n",
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" <td>Aquent</td>\n",
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" <td>2023-04</td>\n",
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" <td>58</td>\n",
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" <td>38</td>\n",
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" <td>50</td>\n",
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" <td>50</td>\n",
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" <td>39</td>\n",
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" <td>36</td>\n",
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" <td>46</td>\n",
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" <td>46</td>\n",
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" <td>43.0</td>\n",
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" <td>27.0</td>\n",
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" <td>24.0</td>\n",
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" <td>26</td>\n",
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" <td>22</td>\n",
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" <td>8.0</td>\n",
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" <td>10.0</td>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>33375202</td>\n",
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" <td>BioLife Solutions, Inc.</td>\n",
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" <td>2023-04</td>\n",
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" <td>11</td>\n",
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" <td>7</td>\n",
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" <td>4</td>\n",
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" <td>11</td>\n",
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" <td>4</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>30867495</td>\n",
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" <td>ZyXel Communications Inc</td>\n",
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" <td>2023-04</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>4</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>1.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>32999202</td>\n",
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" <td>BrightLine Eating Solutions LLC</td>\n",
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" <td>2023-03</td>\n",
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" <td>10,362</td>\n",
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" <td>7,890</td>\n",
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" <td>7,272</td>\n",
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" <td>8,177</td>\n",
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" <td>8,468</td>\n",
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" <td>7,524</td>\n",
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" <td>8,509</td>\n",
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" <td>5,638</td>\n",
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" <td>581.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>30867752</td>\n",
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" <td>Casio America Inc</td>\n",
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" <td>2023-03</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" OrgID Organization Churn Date 2022-01 2022-02 \n",
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"0 30867753 Aquent 2023-04 58 38 \\\n",
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"1 33375202 BioLife Solutions, Inc. 2023-04 11 7 \n",
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"2 30867495 ZyXel Communications Inc 2023-04 1 1 \n",
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"3 32999202 BrightLine Eating Solutions LLC 2023-03 10,362 7,890 \n",
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"4 30867752 Casio America Inc 2023-03 NaN NaN \n",
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"\n",
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" 2022-03 2022-04 2022-05 2022-06 2022-07 2022-08 2022-09 2022-10 2022-11 \n",
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"0 50 50 39 36 46 46 43.0 27.0 24.0 \\\n",
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"1 4 11 4 NaN NaN NaN NaN NaN NaN \n",
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"2 2 1 2 4 2 2 1.0 NaN NaN \n",
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"3 7,272 8,177 8,468 7,524 8,509 5,638 581.0 NaN NaN \n",
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"4 NaN NaN NaN 1 NaN NaN NaN NaN NaN \n",
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"\n",
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" 2022-12 2023-01 2023-02 2023-03 2023-04 \n",
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"0 26 22 8.0 10.0 2.0 \n",
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"1 NaN NaN NaN NaN NaN \n",
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"2 NaN NaN 1.0 1.0 NaN \n",
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"3 NaN NaN NaN NaN NaN \n",
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"4 NaN NaN NaN NaN NaN "
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import sketch\n",
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"cd = pd.read_csv(\"/Users/normrasmussen/Downloads/churned_analysis.csv\")\n",
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"cd.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "b7cf96bb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Replace all NaN values with 0\n",
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"cd.fillna(0, inplace=True)\n",
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"\n",
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"# Remove all commas from strings\n",
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"cd.replace(',','', regex=True, inplace=True)\n",
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"\n",
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"# Convert all strings after the column 'Churn Data' to integers\n",
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"for col in cd.columns[4:]:\n",
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" cd[col] = cd[col].astype(int)\n",
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"\n",
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"cd.drop(columns=['OrgID'], inplace=True)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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